Diving into details: The science behind Conifold Theory

The laws of mechanics and thermodynamics, properly applied to biological neural networks, predict that consciousness emerges naturally from physical systems.

Some current theories of consciousness dismiss the phenomenon altogether, suggesting that the flow of perception and the incredible experience of self-awareness are nothing more than electrochemical pulses in the brain. Other theories of mind describe consciousness in abstract terms, as a cognitive process. A mechanistic explanation of consciousness, which addresses how the brain actually generates mental states, has so far proved elusive. Conifold Theory offers a new perspective.

The incredible experiential nature of neural processing is not something mysterious or impossible to explain. In this view, information content emerges naturally from the physics of ions interacting with a neural membrane structure.

Conifold Theory takes a step-by-step mathematical approach to explain how the instantaneous integration of electrical activity across a three-dimensional neural network structure (such as the human brain) creates information content.

The theory postulates the mind and the brain operate in accordance with the holographic principle, with the mind forming a rich volume of content while the brain encodes that information representationally. The incredible experiential nature of neural processing is not something mysterious or impossible to explain – in this view, information content emerges naturally from the physics of ions interacting with a neural membrane structure.

The encoding of information in neural networks

Neurons produce information, by encoding data about the external environment into their electrochemical state, or the arrangement of charged particles inside and outside the neuron itself. The action potential – an enormous sodium ion flux, which causes a sudden change in voltage across the neural membrane upon detection of coincident upstream signals – encodes useful information, including the qualitative nature of the external stimulus, the intensity of the stimulus, and a level of confidence the stimulus occurred. Setting up an electrochemical gradient across the neuronal membrane allows a neuron to create entropy. Neurons expend a proportional amount of energy to the number of possible system states, to account for each of the ions contributing to the cellular electrochemical potential. This is how information is encoded by a neuron.

Now we can consider consciousness arising from a biological neural network as the result of thermodynamic computation, with energy being distributed toward information processing. The key here is understanding how neurons harness the probabilistic movement of charged particles to gate a state change. Integrating the possible physical states of every ion, relative to the surface of a three-dimensional neural network structure, necessarily generates some set of possible system states for the entire neural network. This set of possible system states is, by definition, the amount of entropy or information held by the system. While the information is encoded by the three-dimensional neural network structure, the information content is represented as a holographic projection, providing a continuous experience of reality.

The emergence of qualitative information content

It may sound strange, but this information content must physically exist in a higher dimension. That is, a mental state is essentially a high-dimensional holographic projection of the physical neural network state. And just as information can be encoded on a two-dimensional surface to generate a three-dimensional holographic projection, information can be encoded on a three-dimensional neural network structure to generate a higher-dimensional holographic projection. That is why mental states do not seem to be in the observable universe, although they are generated by and associated with particular patterns of neural activity. The streaming experience of perception is tied to the encoding structure, and created by a process of thermodynamic computation.

In applying the laws of mechanics to ions moving through a biological neural network, Conifold Theory describes a mechanism by which neural activity can produce thought. Neural networks are thermodynamic systems with a certain volume and temperature, where charged ions cross into a neuron to increase the voltage of that cell. Given that neurons harness the probabilistic motion of charged particles, neurons themselves can be considered probability density functions which encode information representing the sum of all possible particle states. The integration of electrical signals across a neural network therefore generates a network-wide probability density function, which is a sum of all component states. The process of thermodynamic computation effectively converts that network-wide probability density into a defined state, before new probabilities begin to form. This cyclical process of information generation and compression is proposed to occur continuously in the brain, prompting periodic synchronous activity in neurons across the network as uncertainty is resolved and meaning is extracted from a messy dataset. In fact, we know that synchronous activity occurs in the brain, and these coordinated, regular bursts of neural signaling are not well explained by other theories.

In fact, synchronous neural activity is a characteristic of conscious perceptual experience – occurring whenever someone has a ‘click’ of understanding. This coordinated neural activity corresponds to a cohesive, streaming, multi-sensory experience – which can only be accessed by the neural network that produced it. The encoded information is always paired with representative information content. And all those snapshots of the neural network state over a lifetime, paired with all the accompanying percepts, add up to form the model of the self. We are the information sets encoded by our brains.

Thermodynamic computing involves physical information processing

So, due to the intrinsic uncertainty in particle behavior, a neuron which detects the coincident timing of ions crossing a physical barrier can process information in a real physical way, as free energy is converted into entropy. This ‘information’ is encoded in the probabilistic state of each neuron in the network, but the actual information content exists in a conserved manner in a higher-dimensional space, along an axis orthogonal to our three spatial dimensions. That ‘information’ or ‘entropy’ is then compressed as signals are extracted from the noise, and the set of possible system states is reconciled thermodynamically with the surrounding environment.

In this framework, neurons in the central nervous system are acting as qubits, which calculate the probability of reaching a state change, rather than classical computing units, which exist in a simple on-or-off state. In this way, neurons are able to utilize quantum-level noise to calculate the most likely system state overall. Over the course of a single computational cycle, information will be generated and compressed, resolving the neural network state in the present moment and producing a semantic statement about the external environment. These statements are then validated by orienting toward incoming sensory information. These semantical statements are then held in working memory, until they can be integrated with subsequent neural network states, also paired with representational information content, to compute syntactical relationships between events in the local environment. These elaborate cause-effect models of the world can then be used as references to predict outcomes in other situations, based on incoming sensory information and similar past experiences.

Conifold Theory aims to explain cognitive processing

The new approach provides a step-by-step account of cognitive processing, from sensory input to perceptual awareness to the construction of a self with memories and beliefs about the world. It also offers a physical mechanism for top-down control, with the physical process of information compression directly affecting the actualized state of the neural network. Finally, this new theoretical framework specifies the structural and functional requirements for consciousness to manifest in any biological or engineered system. As a result, the theory has broad implications for neuroscience and computing, as well as psychology and mental health practice.

In short, Conifold Theory is a scientific framework which offers the first plausible physical explanation for the seemingly immaterial nature of thought – and a possible mechanism by which mental states play a role in causation by exerting change in the neural network state to achieve goal-directed behavior.